Mixture-Based Modeling of Spatially Correlated Interference in a Poisson Field of Interferers

As the interference in PPP-based wireless networks exhibit spatial correlation, any joint analysis involving multiple spatial points either end up with numerical integrations over
$\mathbb {R}^{2}$
or become analytically too intractable. To tackle these issues, we present an alternate approach which not only offers a simpler analytical structure, but also closely mimics the PPP characteristics. This approach at its core models the correlated interferences using a correlation framework constructed using
random variable mixtures
. In addition, a correlation framework based on the more standard method of
linear combination of random variables
is also presented for comparison purpose. The performance of these models is studied by deriving the joint complementary cumulative distribution function of signal-to-interference ratios at
$N$
arbitrary points. The plots are found to tightly approximate the exact PPP-based results, with the tightness depending on the values of
$\lambda p$
(interferer intensity),
$\alpha $
(path loss exponent), and
$N$
. The applicability of the mixture-based model is also shown for a multi-antennae MRC receiver where only major derivation steps that simplify the outage probability analysis are shown.